The basic objective of an object recognition system is the identification of objects within an image. One object recognition technique involves segmentation and classification. Segmentation refers to a method by which an image is partitioned into independent regions corresponding to portions of objects of interest, which portions have uniform characteristics. Once segmented, these regions are used in a classification process to identify each object in the image.
Often, the images to be processed are color images and, since the color characteristics provide additional information about objects in the images, it is advantageous to segment the images based on common color characteristics. Color characteristics are often represented by values in standardized multi-dimensional color spaces and the images can be provided by high-resolution sensors containing millions of pixels. Because of the complexity of the color space and the quantity of pixels to process, the amount of processing required to segment a high-resolution color image can limit the utility of color image processing.
Conventional segmentation methods use known region-based, edge-based, physical model-based and pixel-based segmentation techniques. Region-based segmentation techniques require prior information about the objects in the image and iterative processes which require relatively large computational resources. Further, such iterative operations are not generally suitable for use in real time applications, such as real time probe alignment in an atomic force microscope.
Edge-based segmentation techniques involve computation of the edge features of the image and assignment of vectors to these features. Segmentation is achieved by clustering edge vectors to form closed-contour regions. The edges are calculated from gradients in specific areas of the image. A difficulty of edge-based segmentation techniques is obtaining closed and connected contours of each object, since the edges often fragment in the image as the result of image variation. In natural scenes, image variations arise from shadows, changes in lighting, color fading and also from artifacts in the imaging equipment.
Physical model-based segmentation methods utilize elementary physical models of the color image formation to produce color variations. However, with these methods, segmented regions do not follow an object's boundaries and segmented perimeters. The boundaries and perimeters instead follow the variation in lighting and color and the models yield accurate segmentation results only in restricted viewing environments, e.g., a controlled environment having controlled lighting and uniform backgrounds.
Pixel-based segmentation methods use gray-scale or color information from each pixel to group the pixels into classes for labeling objects in the image. There are various ways to classify each pixel including histogram-based classification, distance-based pixel classification and maximum likelihood pixel classification. These techniques use only the global information described in an image's feature space, such as the color distribution of the entire image, to classify each pixel in the original image and advantageously, do not require a priori information about the image.
Segmentation further includes clustering by which samples, in the form of pixels in the original image, are grouped into distinct classes. In some conventional histogram-based segmentation methods, clustering involves specifying cluster boundaries (i.e. a volume of pixels in three-dimensional space). Clustering often involves significant iterative computation if several of the clusters are distant from each other in the color space. Other conventional histogram-based segmentation methods use non-iterative clustering algorithms, but require partitioning the image into a multidimensional feature space. The multidimensional feature space is divided into equally spaced volumes referred to as hyper-boxes which include estimated parameters from random field models and other local statistics.